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From "F.Ozgur Catak" <f.ozgur.ca...@gmail.com>
Subject Re: ItemBasedRecommender
Date Fri, 11 Dec 2009 06:43:15 GMT
approx. 100.000 rows and 2000 users

On Fri, Dec 11, 2009 at 2:25 AM, Sean Owen <srowen@gmail.com> wrote:

> The best algorithm really depends on your data.
>
> How many items and how many users do you have? that will determine
> which algorithms will perform better.
>
> Which algorithms will produce the best recommendations is hard to
> tell. Usually you have to use RecommenderEvaluator with lots of
> implementations and your data to find which seems to work best.
>
> if you can say more about your data, maybe I can guess about the best
> implementations to try.
>
> On Thu, Dec 10, 2009 at 9:56 PM, F.Ozgur Catak <f.ozgur.catak@gmail.com>
> wrote:
> > Hi again,
> >
> > Finally I understand the item similarity :). In our b2b project we need
> to
> > develop a recommendation system. I want to use mahout. Is there any best
> > practice. And also another question, is mahout enogh mature to use our
> > production enviroment.
> >
> > thanks
> >
> > On Thu, Dec 10, 2009 at 9:31 PM, Sean Owen <srowen@gmail.com> wrote:
> >
> >> No, the similarity metric is passed in as an ItemSimilarity metric.
> >> There is no implementation based on a model, if that's what you mean.
> >> What else?
> >>
> >> On Thu, Dec 10, 2009 at 7:27 PM, F.Ozgur Catak <f.ozgur.catak@gmail.com
> >
> >> wrote:
> >> > Yes, I read the javadoc but i need the algorithms. For example, does
> >> > recommandation system uses apriori algorithm to find similar values?
> etc.
> >> >
> >> > Maybe it is mine problem, because I'm also a newbi about data mining.
> >> >
> >> > Thanks
> >> >
> >>
> >
>

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